Readers may also be interested in a recent network architecture, called bottom-up/top-down network, which is used in salient object segmentation [1]} and instance segmentation [2]}. We show the configuration of such network in Fig. REF (d), where segmentation features are first generated via traditional bottom-up convolution manner, and then a top-down refinement is proceeded for merging the information from deep to shallow layers into segmentation mask. The main rationale behind this architecture is to generate high-fidelity object masks since deep convolutional layers should lose detailed image information. The bottom-up/top-down network can be seen as a kind of skip-layer network, as different layers are also linked together.
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